English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Learning the differential statistical dependency network between two contextsis essential for many real-life applications, mostly in the high dimensionallow sample regime. In this paper, we propose a novel differential networkestimator that allows integrating various sources of knowledge beyond datasamples. The proposed estimator is scalable to a large number of variables andachieves a sharp asymptotic convergence rate. Empirical experiments onextensive simulated data and four real-world applications (one on neuroimagingand three from functional genomics) show that our approach achieves improveddifferential network estimation and provides better supports to downstreamtasks like classification. Our results highlight significant benefits ofintegrating group, spatial and anatomic knowledge during differential geneticnetwork identification and brain connectome change discovery.

Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge